arXiv subjects

Felix Rauh

Publications and source records attributed to Felix Rauh.

5 recordsLinked to original sources

arXiv2026

Maximum Covering Network Design on Graphs with Low Connectivity: Dynamic Programming and Block-Cut Trees

Planning accessible public services such as health care, emergency response, and schools often requires not only choosing where to open facilities but also improving the network that connects people to them, for example upgrading flood-prone roads in vulnerable regions. Most location models, however, take the network as fixed and the budget as given. We study the Maximum Covering Network Design Problem, in which a single budget is shared between opening facilities and upgrading weak links to maximize the population within a target travel distance of an open facility. The problem is hard even on the simplest networks, and planners usually want to see how coverage grows with the budget, not a single plan. We develop an exact dynamic-programming framework that exploits a property common to real road networks: their low connectivity, with many cut points whose removal disconnects the network. On trees, the recursion is self-contained: each state reduces to a few simple facility and upgrade choices that are fast to compute without a solver, giving predictable running times; for larger budgets and travel distances it outperforms solving the MILP formulation directly. On general low-connectivity networks, the framework decomposes the problem at the cut points and embeds a given MILP formulation to solve the resulting pieces, coordinating them through coverage conditions at the interfaces. This lets us compare a formulation on its own against the same formulation inside the framework: across 306 test cases the framework matches or outperforms direct solving on more than 80% of instances. Because it evaluates all budget levels in a single run, it also yields the full coverage-versus-budget curve at no extra cost, whereas direct solving must split its time across individual budgets.

math.OC↗

arXiv2023

Nonlinear Isometric Manifold Learning for Injective Normalizing Flows

To model manifold data using normalizing flows, we employ isometric autoencoders to design embeddings with explicit inverses that do not distort the probability distribution. Using isometries separates manifold learning and density estimation and enables training of both parts to high accuracy. Thus, model selection and tuning are simplified compared to existing injective normalizing flows. Applied to data sets on (approximately) flat manifolds, the combined approach generates high-quality data.

cs.LG↗

arXiv2023

Solution-processed NiPS3 thin films from Liquid Exfoliated Inks with Long-Lived Spin-Entangled Excitons

Antiferromagnets are promising materials for future opto-spintronic applications since they show spin dynamics in the THz range and no net magnetization. Recently, layered van der Waals (vdW) antiferromagnets have been reported, which combine low-dimensional excitonic properties with complex spin-structure. While various methods for the fabrication of vdW 2D crystals exist, formation of large area and continuous thin films is challenging because of either limited scalability, synthetic complexity, or low opto-spintronic quality of the final material. Here, we fabricate centimeter-scale thin films of the van der Waals 2D antiferromagnetic material NiPS3, which we prepare using a crystal ink made from liquid phase exfoliation (LPE). We perform statistical atomic force microscopy (AFM) and scanning electron microscopy (SEM) to characterize and control the lateral size and number of layers through this ink-based fabrication. Using ultrafast optical spectroscopy at cryogenic temperatures, we resolve the dynamics of photoexcited excitons. We find antiferromagnetic spin arrangement and spin-entangled Zhang-Rice multiplet excitons with lifetimes in the nanosecond range, as well as ultranarrow emission linewidths, despite the disordered nature of our films. Thus, our findings demonstrate scalable thin-film fabrication of high-quality NiPS3, which is crucial for translating this 2D antiferromagnetic material into spintronic and nanoscale memory devices and further exploring its complex spin-light coupled states.

physics.chem-ph↗

arXiv2022

Spatially-Modulated Silicon Interface Energetics via Hydrogen Plasma-Assisted Atomic Layer Deposition of Ultrathin Alumina

Atomic layer deposition (ALD) is a key technique for the continued scaling of semiconductor devices, which increasingly relies on reproducible and scalable processes for interface manipulation of 3D structured surfaces on the atomic scale. While ALD allows the synthesis of conformal films at low temperature with utmost control over the thickness, atomically-defined closed coatings and surface modifications are still extremely difficult to achieve because of three-dimensional growth during nucleation. Here, we present a route towards sub-nanometer thin and continuous aluminum oxide (AlOx) coatings on silicon (Si) substrates for the spatial control of the surface charge density and interface energetics. We use trimethylaluminum (TMA) in combination with remote hydrogen plasma instead of a gas-phase oxidant for the transformation of silicon oxide into alumina (AlOx). During the initial ALD cycles, TMA reacts with the surface oxide (SiO2) on silicon until there is a saturation of bindings sites, after which the oxygen from the underlying surface oxide is consumed, thereby transforming the silicon oxide into Si capped with AlOx. Depending on the number of ALD cycles, the SiO2 can be partially or fully transformed, which we exploit to create sub-nanometer thin and continuous AlOx layers deposited in selected regions defined by lithographic patterning. The resulting patterned surfaces are characterized by lateral AlOx/SiO2 interfaces possessing step heights as small as 0.3 nm and surface potential steps in excess of 0.4 V. In addition, the introduction of fixed negative charges of $9 \times 10^{12}$ cm$^{-2}$ enables modulation of the surface band bending, which is relevant to the field-effect passivation of Si and low-impedance charge transfer across contact interfaces.

cond-mat.mtrl-sci↗

arXiv2021

The Dial-a-Ride Problem in Primary Care with Flexible Scheduling

Patient transportation systems are instrumental in lowering access barriers in primary care by taking patients to their GPs. As part of this setting, each transportation request of a chronic or walk-in patient consists of an outbound trip to the GP and an inbound trip back home. The economic sustainability of patient transportation systems mainly depends on their utilization and how well transportation requests can be bundled through ride sharing. To ease the latter, we consider a flexible scheduling of chronic patients in which only a certain range for an appointment is fixed a priori while the exact time is determined by the scheduling of the outbound trip. This leads to a novel extension of the dial-a-ride problem that we call the dial-a-ride problem with combined requests and flexible scheduling (DARPCF). In this paper, we introduce two heuristics for the DARPCF that exploit this increased flexibility. Both approaches initially compute so-called mini-clusters of outbound requests. Then, the mini-clusters are linked by (i) solving a traveling salesman problem and creating routes of outbound rides with a splitting procedure or by (ii) using a rolling horizon approach and solving bipartite matching problems for the vehicle assignment. Our computational study shows that by using the presented algorithms with the flexible scheduling of chronic appointments, the average number of served requests can be increased by 16% compared to a non-flexible setting.

math.OC↗